Machine Learning Architect (SAS Viya) (8 months contract)
George Bernard Consulting · Sri Lanka · posted today ago
Listing supplied by Himalayas. 101 Careers did not originate this post and applications are handled by the employer.
About this role
- Define and own the end-to-end machine learning architecture for financial and taxation platforms
- Design scalable, secure, and compliant ML solutions using SAS Viya and complementary ML technologies
- Establish architectural standards, best practices, and governance frameworks for enterprise ML systems
- Lead the design of data ingestion, feature engineering, model training, deployment, and monitoring pipelines
- Ensure ML solutions comply with regulatory, audit, data privacy, and risk management requirements
- Define and enforce MLOps standards including model lifecycle management, versioning, explainability, and performance monitoring
- Collaborate with finance, taxation, compliance, and risk stakeholders to translate business and regulatory needs into technical solutions
- Review and approve ML designs, pipelines, and deployment strategies across teams
- Evaluate and introduce new ML technologies and platforms aligned with enterprise and regulatory needs
- Provide technical leadership and mentorship to senior ML engineers and teams
Requirements
- Bachelors or Masters degree in Computer Science, Data Science, Engineering, Statistics, or a related field
- 8+ years of experience in data, analytics, or machine learning roles, with at least 4+ years in architecture or technical leadership positions
- Strong domain experience in financial services, taxation, risk, or regulatory analytics
- Extensive hands-on and architectural experience with SAS Viya
- Deep understanding of machine learning algorithms, statistical modeling, and financial data analytics
- Strong expertise in Python and ML libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch
- Proven experience with MLOps practices, model governance, explainable AI, and risk controls
- Experience designing and deploying ML solutions on cloud platforms such as AWS, Azure, or GCP
- Strong SQL skills and experience working with large-scale financial datasets
- Knowledge of big data or distributed processing frameworks such as Spark is an advantage
Originally posted on Himalayas
